#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Callable, List, Optional, Tuple, Union
import re
from dataclasses import dataclass
import html
import inspect
import urllib.parse as ul

import torch
import torch_npu
from transformers import T5EncoderModel, T5Tokenizer

from diffusers.models import AutoencoderKL, Transformer2DModel
from diffusers.schedulers import DPMSolverMultistepScheduler
from diffusers.utils import (
    BACKENDS_MAPPING,
    is_bs4_available,
    is_ftfy_available,
    replace_example_docstring,
)
from diffusers.utils.torch_utils import randn_tensor
from diffusers.utils import BaseOutput
from opensoraplan.utils.log import logger
from opensoraplan import LatteParams
from .pipeline_utils import OpenSoraPlanPipelineBase

TENSOR_TYPE_PT = "pt"
SUPPORT_VIDEO_LEN = [5, 17]
SUPPORT_IMAGE_SIZE = [256, 512]


if is_bs4_available():
    from bs4 import BeautifulSoup

if is_ftfy_available():
    import ftfy

EXAMPLE_DOC_STRING = """
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import PixArtAlphaPipeline

        >>> # You can replace the checkpoint id with "PixArt-alpha/PixArt-XL-2-512x512" too.
        >>> pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", torch_dtype=torch.float16)
        >>> # Enable memory optimizations.
        >>> pipe.enable_model_cpu_offload()

        >>> prompt = "A small cactus with a happy face in the Sahara desert."
        >>> image = pipe(prompt).images[0]
        ```
"""


@dataclass
class LatentsParams:
    batch_size: int
    num_channels_latents: int
    video_length: int
    height: int
    width: int
    dtype: torch.dtype
    device: torch.device


@dataclass
class InputParams:
    prompt: str
    height: int
    width: int
    negative_prompt: str
    callback_steps: int


@dataclass
class VideoPipelineOutput(BaseOutput):
    video: torch.Tensor


class OpenSoraPlanPipeline(OpenSoraPlanPipelineBase):
    r"""
    pipeline for text-to-image generation using PixArt-Alpha.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`T5EncoderModel`]):
            Frozen text-encoder. PixArt-Alpha uses
            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
            [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
        tokenizer (`T5Tokenizer`):
            Tokenizer of class
            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
        transformer ([`Transformer2DModel`]):
            A text conditioned `Transformer2DModel` to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
    """
    bad_punct_regex = re.compile(
        r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
    )  # noqa

    _optional_components = ["tokenizer", "text_encoder"]
    model_cpu_offload_seq = "text_encoder->transformer->vae"

    def __init__(
            self,
            tokenizer: T5Tokenizer,
            text_encoder: T5EncoderModel,
            vae: AutoencoderKL,
            transformer: Transformer2DModel,
            scheduler: DPMSolverMultistepScheduler,
            video_length: int = 17,
            image_size: int = 256
    ):
        super().__init__()
        if video_length not in SUPPORT_VIDEO_LEN:
            raise ValueError("Input video_length is not supported.")

        if image_size not in SUPPORT_IMAGE_SIZE:
            raise ValueError("Input image_size is not supported.")

        torch.set_grad_enabled(False)

        self.text_encoder = text_encoder
        self.tokenizer = tokenizer
        self.transformer = transformer
        self.vae = vae
        self.scheduler = scheduler
        self.video_length = video_length
        self.image_size = image_size
        self.cfg_last_step = 10000

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
            self,
            prompt: Union[str, List[str]] = None,
            num_inference_steps: int = 20,
            guidance_scale: float = 4.5,
            num_images_per_prompt: Optional[int] = 1,
            enable_temporal_attentions: bool = True,
    ) -> Union[VideoPipelineOutput, Tuple]:
        """
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            num_inference_steps (`int`, *optional*, defaults to 100):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.0):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            enable_temporal_attentions (`bool`, *optional*, defaults to True):
                Whether to enable temporal attentions, if force images, the value should be set False.
        Examples:

        Returns:
            [`~pipelines.ImagePipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
                returned where the first element is a list with the generated images
        """
        # 1. Check inputs. Raise error if not correct
        negative_prompt = ""
        eta = 0.0
        generator = None
        latents = None
        prompt_embeds = None
        negative_prompt_embeds = None
        output_type = "pil"
        return_dict = True
        callback = None
        callback_steps = 1
        clean_caption = True
        mask_feature = True

        height = width = self.image_size
        input_params = InputParams(prompt, height, width, negative_prompt, callback_steps)
        self._check_inputs(input_params, prompt_embeds, negative_prompt_embeds)
        if num_inference_steps < 4 or num_inference_steps > 300:
            raise ValueError("num_inference_steps should be in the range of [4, 300].")
        if self.transformer.cache_manager.start_step < 0 or \
            self.transformer.cache_manager.start_step > (num_inference_steps - 1):
            raise ValueError("start_step should be in the range of [0, num_inference_steps-1]")
        if self.transformer.cache_manager.step_interval < 1 or \
            self.transformer.cache_manager.step_interval > (num_inference_steps - 2):
            raise ValueError("step_interval should be in the range of [1, num_inference_steps-2]")
        if num_images_per_prompt < 1 or num_images_per_prompt > 100:
            raise ValueError("num_images_per_prompt should be in the range of [1, 100].")
        if self.cfg_last_step < 0:
            raise ValueError("cfg_last_step should be not less than 0.")

        # 2. Default height and width to transformer
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        device = self.text_encoder.device or self._execution_device

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompt
        prompt_embeds, negative_prompt_embeds = self._encode_prompt(
            prompt,
            do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            num_images_per_prompt=num_images_per_prompt,
            device=device,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            clean_caption=clean_caption,
            mask_feature=mask_feature,
        )
        torch.npu.empty_cache()

        if do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps

        # 5. Prepare latents.
        latent_channels = self.transformer.config.in_channels
        latents_params = LatentsParams(batch_size * num_images_per_prompt, latent_channels, self.video_length,
                                       height, width, prompt_embeds.dtype, device)
        latents = self._prepare_latents(latents_params, generator, latents)

        # 6. Prepare extra step kwargs.
        extra_step_kwargs = self._prepare_extra_step_kwargs(generator, eta)

        # 6.1 Prepare micro-conditions.
        added_cond_kwargs = {"resolution": None, "aspect_ratio": None}

        # 7. Denoising loop
        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)

        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                if i == self.cfg_last_step:
                    prompt_embeds = prompt_embeds[1:2]
                if i >= self.cfg_last_step:
                    do_classifier_free_guidance = False

                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                current_timestep = t
                if not torch.is_tensor(current_timestep):
                    # This would be a good case for the `match` statement (Python 3.10+)
                    is_mps = latent_model_input.device.type == "mps"
                    if isinstance(current_timestep, float):
                        dtype = torch.float32 if is_mps else torch.float64
                    else:
                        dtype = torch.int32 if is_mps else torch.int64
                    current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
                elif len(current_timestep.shape) == 0:
                    current_timestep = current_timestep[None].to(latent_model_input.device)
                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                current_timestep = current_timestep.expand(latent_model_input.shape[0])

                latte_params = LatteParams(
                    hidden_states=latent_model_input,
                    encoder_hidden_states=prompt_embeds,
                    timestep=current_timestep,
                    added_cond_kwargs=added_cond_kwargs,
                    enable_temporal_attentions=enable_temporal_attentions,
                )
                # predict noise model_output
                noise_pred = self.transformer(
                    latte_params,
                    t_idx=i,
                )[0]

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                # learned sigma
                if self.transformer.config.out_channels // 2 == latent_channels:
                    noise_pred = noise_pred.chunk(2, dim=1)[0]
                else:
                    noise_pred = noise_pred

                # compute previous image: x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        step_idx = i // getattr(self.scheduler, "order", 1)
                        callback(step_idx, t, latents)

        if not output_type == 'latents':
            video = self._decode_latents(latents)
        else:
            video = latents
            return VideoPipelineOutput(video=video)

        if not return_dict:
            return (video,)

        return VideoPipelineOutput(video=video)

    # Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/utils.py
    def _mask_text_embeddings(self, emb, mask):
        if emb.shape[0] == 1:
            keep_index = mask.sum().item()
            return emb[:, :, :keep_index, :], keep_index  # 1, 120, 4096 -> 1 7 4096
        else:
            masked_feature = emb * mask[:, None, :, None]  # 1 120 4096
            return masked_feature, emb.shape[2]

    # Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt
    def _encode_prompt(
            self,
            prompt: Union[str, List[str]],
            do_classifier_free_guidance: bool = True,
            negative_prompt: str = "",
            num_images_per_prompt: int = 1,
            device: Optional[torch.device] = None,
            prompt_embeds: Optional[torch.FloatTensor] = None,
            negative_prompt_embeds: Optional[torch.FloatTensor] = None,
            clean_caption: bool = False,
            mask_feature: bool = True,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`
                instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). For
                PixArt-Alpha, this should be "".
            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
                whether to use classifier free guidance or not
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                number of images that should be generated per prompt
            device: (`torch.device`, *optional*):
                torch device to place the resulting embeddings on
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. For PixArt-Alpha, it's should be the embeddings of the ""
                string.
            clean_caption (bool, defaults to `False`):
                If `True`, the function will preprocess and clean the provided caption before encoding.
            mask_feature: (bool, defaults to `True`):
                If `True`, the function will mask the text embeddings.
        """
        embeds_initially_provided = prompt_embeds is not None and negative_prompt_embeds is not None

        if device is None:
            device = self.text_encoder.device or self._execution_device

        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        # See Section 3.1. of the paper.
        max_length = 300

        if prompt_embeds is None:
            prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_attention_mask=True,
                add_special_tokens=True,
                return_tensors=TENSOR_TYPE_PT,
            )
            text_input_ids = text_inputs.input_ids
            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors=TENSOR_TYPE_PT).input_ids

            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                    text_input_ids, untruncated_ids
            ):
                removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1: -1])
                logger.warning(
                    "The following part of your input was truncated because the model can only handle sequences up to"
                    f" {max_length} tokens: {removed_text}"
                )

            attention_mask = text_inputs.attention_mask.to(device)
            prompt_embeds_attention_mask = attention_mask

            prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
            prompt_embeds = prompt_embeds[0]
        else:
            prompt_embeds_attention_mask = torch.ones_like(prompt_embeds)

        if self.text_encoder is not None:
            dtype = self.text_encoder.dtype
        elif self.transformer is not None:
            dtype = self.transformer.dtype
        else:
            dtype = None

        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)

        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
        prompt_embeds_attention_mask = prompt_embeds_attention_mask.view(bs_embed, -1)
        prompt_embeds_attention_mask = prompt_embeds_attention_mask.repeat(num_images_per_prompt, 1)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens = [negative_prompt] * batch_size
            uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_attention_mask=True,
                add_special_tokens=True,
                return_tensors=TENSOR_TYPE_PT,
            )
            attention_mask = uncond_input.attention_mask.to(device)

            negative_prompt_embeds = self.text_encoder(
                uncond_input.input_ids.to(device),
                attention_mask=attention_mask,
            )
            negative_prompt_embeds = negative_prompt_embeds[0]

        if do_classifier_free_guidance:
            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = negative_prompt_embeds.shape[1]

            negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)

            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
        else:
            negative_prompt_embeds = None

        # Perform additional masking.
        if mask_feature and not embeds_initially_provided:
            prompt_embeds = prompt_embeds.unsqueeze(1)
            masked_prompt_embeds, keep_indices = self._mask_text_embeddings(prompt_embeds, prompt_embeds_attention_mask)
            masked_prompt_embeds = masked_prompt_embeds.squeeze(1)
            masked_negative_prompt_embeds = (
                negative_prompt_embeds[:, :keep_indices, :] if negative_prompt_embeds is not None else None
            )

            return masked_prompt_embeds, masked_negative_prompt_embeds

        return prompt_embeds, negative_prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.
    # prepare_extra_step_kwargs
    def _prepare_extra_step_kwargs(self, generator, eta):
        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]

        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

    def _check_inputs(
            self,
            inpt_params,
            prompt_embeds=None,
            negative_prompt_embeds=None,
    ):
        if inpt_params.height % 8 != 0 or inpt_params.width % 8 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 8 but are "
                             f"{inpt_params.height} and {inpt_params.width}.")

        callback_not_none = ((inpt_params.callback_steps is not None) and
                             (not isinstance(inpt_params.callback_steps, int) or inpt_params.callback_steps <= 0))
        if (inpt_params.callback_steps is None) or callback_not_none:
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {inpt_params.callback_steps} of type"
                f" {type(inpt_params.callback_steps)}."
            )

        if inpt_params.prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {inpt_params.prompt} and `prompt_embeds`: {prompt_embeds}. "
                f"Please make sure to only forward one of the two."
            )
        elif inpt_params.prompt is None and prompt_embeds is None:
            raise ValueError(
                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
            )
        elif inpt_params.prompt is not None and (not isinstance(inpt_params.prompt, str) and
                                                 not isinstance(inpt_params.prompt, list)):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(inpt_params.prompt)}")

        if inpt_params.prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {inpt_params.prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        if inpt_params.negative_prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt`: {inpt_params.negative_prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        if prompt_embeds is not None and negative_prompt_embeds is not None:
            if prompt_embeds.shape != negative_prompt_embeds.shape:
                raise ValueError(
                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
                    f" {negative_prompt_embeds.shape}."
                )

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
    def _prepare_latents(self, latents_params, generator, latents=None):
        shape = (
            latents_params.batch_size,
            latents_params.num_channels_latents,
            latents_params.video_length,
            self.vae.latent_size[0],
            self.vae.latent_size[1]
        )
        if isinstance(generator, list) and len(generator) != latents_params.batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {latents_params.batch_size}. Make sure the batch size matches the length of the generators."
            )

        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=latents_params.device, dtype=latents_params.dtype)
        else:
            latents = latents.to(latents_params.device)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        return latents

    def _decode_latents(self, latents):
        video = self.vae.decode(latents)
        video = ((video / 2.0 + 0.5).clamp(0, 1) * 255).to(dtype=torch.uint8).cpu().permute(0, 1, 3, 4, 2).contiguous()
        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
        return video

    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
    def _text_preprocessing(self, text, clean_caption=False):
        if clean_caption and not is_bs4_available():
            logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
            logger.warning("Setting `clean_caption` to False...")
            clean_caption = False

        if clean_caption and not is_ftfy_available():
            logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
            logger.warning("Setting `clean_caption` to False...")
            clean_caption = False

        if not isinstance(text, (tuple, list)):
            text = [text]

        def process(text: str):
            if clean_caption:
                text = self._clean_caption(text)
                text = self._clean_caption(text)
            else:
                text = text.lower().strip()
            return text

        return [process(t) for t in text]

    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
    def _clean_caption(self, caption):
        caption = str(caption)
        caption = ul.unquote_plus(caption)
        caption = caption.strip().lower()
        caption = re.sub("<person>", "person", caption)
        # urls:
        caption = re.sub(
            r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.]"
            r"(?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",
            # noqa
            "",
            caption,
        )  # regex for urls
        caption = re.sub(
            r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)"
            r"[\w/-]*\b\/?(?!@)))",
            # noqa
            "",
            caption,
        )  # regex for urls
        # html:
        caption = BeautifulSoup(caption, features="html.parser").text

        # @<nickname>
        caption = re.sub(r"@[\w\d]+\b", "", caption)

        # 31C0—31EF CJK Strokes
        # 31F0—31FF Katakana Phonetic Extensions
        # 3200—32FF Enclosed CJK Letters and Months
        # 3300—33FF CJK Compatibility
        # 3400—4DBF CJK Unified Ideographs Extension A
        # 4DC0—4DFF Yijing Hexagram Symbols
        # 4E00—9FFF CJK Unified Ideographs
        caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
        caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
        caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
        caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
        caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
        caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
        caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
        #######################################################

        # все виды тире / all types of dash --> "-"
        caption = re.sub(
            r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030"
            r"\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+",
            # noqa
            "-",
            caption,
        )

        # кавычки к одному стандарту
        caption = re.sub(r"[`´«»“”¨]", '"', caption)
        caption = re.sub(r"[‘’]", "'", caption)

        # &quot;
        caption = re.sub(r"&quot;?", "", caption)
        # &amp
        caption = re.sub(r"&amp", "", caption)

        # ip adresses:
        caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)

        # article ids:
        caption = re.sub(r"\d:\d\d\s+$", "", caption)

        # \n
        caption = re.sub(r"\\n", " ", caption)

        # "#123"
        caption = re.sub(r"#\d{1,3}\b", "", caption)
        # "#12345.."
        caption = re.sub(r"#\d{5,}\b", "", caption)
        # "123456.."
        caption = re.sub(r"\b\d{6,}\b", "", caption)
        # filenames:
        caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)

        #
        caption = re.sub(r"[\"\']{2,}", r'"', caption)  # """AUSVERKAUFT"""
        caption = re.sub(r"[\.]{2,}", r" ", caption)  # """AUSVERKAUFT"""

        caption = re.sub(self.bad_punct_regex, r" ", caption)  # ***AUSVERKAUFT***, #AUSVERKAUFT
        caption = re.sub(r"\s+\.\s+", r" ", caption)  # " . "

        # this-is-my-cute-cat / this_is_my_cute_cat
        regex2 = re.compile(r"(?:\-|\_)")
        if len(re.findall(regex2, caption)) > 3:
            caption = re.sub(regex2, " ", caption)

        caption = ftfy.fix_text(caption)
        caption = html.unescape(html.unescape(caption))

        caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption)  # jc6640
        caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption)  # jc6640vc
        caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption)  # 6640vc231

        caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
        caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
        caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
        caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
        caption = re.sub(r"\bpage\s+\d+\b", "", caption)

        caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption)  # j2d1a2a...

        caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)

        caption = re.sub(r"\b\s+\:\s+", r": ", caption)
        caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
        caption = re.sub(r"\s+", " ", caption)

        caption.strip()

        caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
        caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
        caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
        caption = re.sub(r"^\.\S+$", "", caption)

        return caption.strip()